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This paper aims to propose a dynamic parameter identification method based on sensitivity analysis for the 5-degree of freedom (DOF) hybrid robots, to solve the problems of too many identification parameters, complex model, difficult convergence of optimization algorithms and easy-to-fall into a locally optimal solution, and improve the efficiency and accuracy of dynamic parameter identification.

First, the dynamic parameter identification model of the 5-DOF hybrid robot was established based on the principle of virtual work. Then, the sensitivity of the parameters to be identified is analyzed by Sobol’s sensitivity method and verified by simulation. Finally, an identification strategy based on sensitivity analysis was designed, experiments were carried out on the real robot and the results were verified.

Compared with the traditional full-parameter identification method, the dynamic parameter identification method based on sensitivity analysis proposed in this paper converges faster when optimized using the genetic algorithm, and the identified dynamic model has higher prediction accuracy for joint drive forces and torques than the full-parameter identification models.

This work analyzes the sensitivity of the parameters to be identified in the dynamic parameter identification model for the first time. Then a parameter identification method is proposed based on the results of the sensitivity analysis, which can effectively reduce the parameters to be identified, simplify the identification model, accelerate the convergence of the optimization algorithm and improve the prediction accuracy of the identified model for the joint driving forces and torques.

This study aims to model the important flow response quantities over a shrinking wedge with the help of response surface methodology (RSM) and an artificial neural network (ANN). An ANN simulation for optimal thermal transport of incompressible viscous fluid under the impact of the magnetic effect (MHD) over a shrinking wedge with sensitivity analysis and optimization with RSM has yet not been investigated. This effort is devoted to filling the gap in existing literature.

A statistical experimental design is a setup with RSM using a central composite design (CCD). This setup involves the combination of values of input parameters such as porosity, shrinking and magnetic effect. The responses of skin friction coefficient and Nusselt number are required against each parameter combination of the experimental design, which is computed by solving the simplified form of the governing equations using bvp4c (a built-in technique in MATLAB). An empirical model for Cf_x and Nu_x using RSM and ANN adopting the Levenberg–Marquardt algorithm based on trained neural networks (LMA-TNN) is attained. The empirical model for skin friction coefficient and Nusselt number using RSM has 99.96% and 99.99% coefficients of determination, respectively.

The values of these matrices show the goodness of fit for these quantities. The authors compared the results obtained from bvp4c, RSM and ANN and found them all to be in good agreement. A sensitivity analysis is performed, which shows that Cf_x as well as Nu_x are most affected by porosity. However, they are least affected by magnetic parameters.

This study aims to simulate ANN and sensitivity analysis for optimal thermal transport of magnetic viscous fluid over shrinking wedge.

As one of the tomographic imaging techniques, electrical capacitance tomography (ECT) is widely used in many industrial applications. While most ECT sensors have electrodes placed around a cylindrical chamber, the planar ECT sensor has been investigated for depth and defect detection. However, the planar ECT sensor has limited height and depth sensing capability due to its single-sided assessment with the use of only a single-plane design. The purpose of this paper is to investigate a dual-plane miniature planar 3D ECT sensor design using the 3 × 3 matrix electrode array.

The sensitivity map of dual-plane miniature planar 3D ECT sensor was analysed using 3D visualisation, the singular value decomposition and the axial resolution analysis. Then, the sensor was fabricated for performance analysis based on 3D imaging experiments.

The sensitivity map analysis showed that the dual-plane miniature planar 3D ECT sensor has enhanced the height sensing capability, and it is less ill-posed in 3D image reconstruction. The dual-plane miniature planar 3D ECT sensor showed a 28% improvement in reconstructed 3D image quality as compared to the single-plane sensor set-up.

The 3 × 3 matrix electrode array has been proposed to use only the necessary electrode pair combinations for image reconstruction. Besides, the increase in number of electrodes from the dual-plane sensor setup improved the height reconstruction of the test sample.

A parabolic trough solar collector is an advanced concentrated solar power technology that significantly captures radiant energy. Solar power will help different sectors reach their energy needs in areas where traditional fuels are in use. This study aims to examine the sensitivity analysis for optimizing the heat transfer and entropy generation in the Jeffrey magnetohydrodynamic hybrid nanofluid flow under the influence of motile gyrotactic microorganisms with solar radiation in the parabolic trough solar collectors. The influences of viscous dissipation and Ohmic heating are also considered in this investigation.

Governing partial differential equations are derived via boundary layer assumptions and nondimensionalized with the help of suitable similarity transformations. The resulting higher-order coupled ordinary differential equations are numerically investigated using the Runga-Kutta fourth-order numerical approach with the shooting technique in the computational MATLAB tool.

The numerical outcomes of influential parameters are presented graphically for velocity, temperature, entropy generation, Bejan number, drag coefficient and Nusselt number. It is observed that escalating the values of melting heat parameter and the Prandl number enhances the Nusselt number, while reverse effect is observed with an enhancement in the magnetic field parameter and bioconvection Lewis number. Increasing the magnetic field and bioconvection diffusion parameter improves the entropy and Bejan number.

Nanotechnology has captured the interest of researchers due to its engrossing performance and wide range of applications in heat transfer and solar energy storage. There are numerous advantages of hybrid nanofluids over traditional heat transfer fluids. In addition, the upswing suspension of the motile gyrotactic microorganisms improves the hybrid nanofluid stability, enhancing the performance of the solar collector. The use of solar energy reduces the industry’s dependency on fossil fuels.

The purpose of this paper is to achieve the multi-objective optimization design of novel tubular switched reluctance motor (TSRM).

First, the structure and initial dimensions of TSRM are obtained based on design criteria and requirements. Second, the sensitivity analysis rules, process and results of TSRM are performed. Third, three optimization objectives are determined by the average electromagnetic force, smoothing coefficient and copper loss ratio. The analytic hierarchy process-entropy method-a technique for order preference by similarity to an ideal solution-grey relation analysis comprehensive evaluation algorithm is used to optimize TSRM. Finally, a prototype is manufactured, a hardware platform is built and static and dynamic experimental validations are carried out.

The sensitivity analysis reveals that parameters significantly impact the performance of TSRM. The results of multi-objective optimization show that the average electromagnetic force and smoothing coefficient after optimization are better than before, and the copper loss ratio reduces slightly. The experimental and simulated results of TSRM are consistent, which verifies the accuracy of TSRM.

In this paper, only three optimization objectives are selected in the multi-objective optimization process. To improve the performance of TSRM, the heating characteristics, such as iron loss, can be considered as the optimization objective for a more comprehensive analysis of TSRM performance.

A novel motor structure is designed, combining the advantages of the TSRM and the linear motor. The established sensitivity analysis rules are scientific and suitable for the effects of various parameters on motor performance. The proposed multi-objective optimization algorithm is a comprehensive evaluation algorithm. It considers subjective weight and objective weight and fully uses the original data and the relational degree between the optimization objectives.

The promotion of autonomous vehicles introduces privacy and security risks, underscoring the pressing need for responsible innovation implementation. To more effectively address the societal risks posed by autonomous vehicles, considering collaborative engagement of key stakeholders is essential. This study aims to provide insights into the governance of potential privacy and security issues in the innovation of autonomous driving technology by analyzing the micro-level decision-making processes of various stakeholders.

For this study, the authors use a nuanced approach, integrating key stakeholder theory, perceived value theory and prospect theory. The study constructs a model based on evolutionary game for the privacy and security governance mechanism of autonomous vehicles, involving enterprises, governments and consumers.

The governance of privacy and security in autonomous driving technology is influenced by key stakeholders’ decision-making behaviors and pivotal factors such as perceived value factors. The study finds that the governmental is influenced to a lesser extent by the decisions of other stakeholders, and factors such as risk preference coefficient, which contribute to perceived value, have a more significant influence than appearance factors like participation costs.

This study lacks an investigation into the risk sensitivity of various stakeholders in different scenarios.

The study delineates the roles and behaviors of key stakeholders and contributes valuable insights toward addressing pertinent risk concerns within the governance of autonomous vehicles. Through the study, the practical application of Responsible Innovation theory has been enriched, addressing the shortcomings in the analysis of micro-level processes within the framework of evolutionary game.

This study focused on identifying critical factors governing the fire response of prestressed hollow-core slabs. The hollow-core slabs used as flooring units can be subjected to elevated temperatures during a fire. The fire response of prestressed hollow-core slabs is required to develop slabs with greater fire endurance. The present study aims to determine the extent to which the hollow-core slab can sustain load during a fire without undergoing progressive collapse under extreme fire and heating scenarios.

A finite element model was generated to predict the fire response of prestressed hollow core slabs under elevated temperatures. The accuracy of the model was predicted by examining thermal and structural responses through coupled temperature displacement analysis. A sensitivity analysis was performed to study the effects of concrete properties on prediction of system response. A parametric study was conducted by varying the thickness of the slab, fire and heating scenarios.

Thermal conductivity and specific heat of concrete were determined as sensitive parameters. The thickness of the slab was identified as a critical factor at a higher load level. Asymmetric heating of the slab resulted in higher fire resistance compared with symmetric heating.

This is the first study focused on studying the effect of modeling uncertainties on the system response by sensitivity analysis under elevated temperatures. The developed model with a parametric study helps in identifying critical factors for design purposes.

This study aims to explore the relationship between risk governance characteristics (chief risk officer [CRO], chief financial officer [CFO] and senior directors [SENIOR]) and regulatory adjustments (RAs) in Organization for Economic Cooperation and Development public commercial banks.

Using principal component analysis (PCA) and regression models, the research analyzes a representative data set of these banks.

A significant negative correlation between risk governance characteristics and RAs is found. Sensitivity analysis on the regulatory Tier 1 capital ratio and the total capital ratio indicates mixed outcomes, suggesting a complex relationship that warrants further exploration.

The study’s limited sample size calls for further research to confirm findings and explore risk governance’s impact on banks’ capital structures.

Enhanced risk governance could reduce RAs, influencing banking policy.

The study advocates for improved banking regulatory practices, potentially increasing sector stability and public trust.

This study contributes to understanding risk governance’s role in regulatory compliance, offering insights for policymaking in banking.

The main stance of this paper is to draw an authentic and rigorous outlook in terms of the financial and operational performance of small lodging establishments (SLEs) and put forth achievable and practical economic solutions that demonstrate the relative effectiveness of the adopted measures. This paper also suggests practical solutions to help minimize SLEs' financial vulnerability to long-term crisis and to boost their resilience with relative measures by applying recovery revival strategies for this particular segment of the lodging industry.

The authors have picked a locally owned resort hotel in Central Florida area and structured a real-life, case study-based inductive approach that is purposeful and offers rich economic outlook and analysis for the entire lodging industry, especially for the resort-hotel type of accommodation facilities. The main reason for why they only focus on one company is that they can fully understand the financial effects of COVID-19 on resort type of hotels and layout countering strategies. To achieve paper objectives, they have implemented cost–benefit (C–B), break-even (B-E) analyses along with a sensitivity testing approach.

The most striking result was that during the state-mandated shutdown period in 2020, overhead and overall operational costs associated with room sales and revenues were very high during this period that shrank the contribution margin ratio for rooms CMRw (room) and eventually yielded high sales volumes to be achieved at the B-E points vs lower sales volumes with almost the same average daily rate (ADR) levels needed for the B-E levels.

Future studies should specifically delve further into a portfolio of SLEs in the region or state or nation wise because the units comprising the SLEs might be too small to muster the changes required to bounce forward for the entire lodging industry in the world.

The resort's revenue re-optimization focus should center on financial re-benchmarking and business re-viability stress under different levels of shock scenarios. According to the different scenarios and calibrations for the ADRs, room nights, net present values (NPVs) of cash flows and profit margins derived from our main analyses, minimizing expenses and preserving cash would be the best key strategy for financial recovery during an ongoing COVID-19 pandemic.

It is obvious that the lodging, hospitality and tourism industry are the hardest-hit industries by the harsh and adverse effects of COVID-19. The effects of pandemic are differently shaped on operations in different industries and subsectors. Therefore, the operational and financial evaluation for the SLEs as the core and a catalyst in the entire lodging industry can shed a light on the strategic financial recovery procedures with broadly applicable real-life and endogenous capabilities and reasoning.

This study aims to establish a reliable index to identify interdisciplinary breakthrough innovation effectively. We constructed a new index, the D_Div index, for this purpose.

The D_Div index incorporates the degree of interdisciplinarity in the breakthrough index. To validate the index, a data set combining the publication records and citations of Nobel Prize laureates was divided into experimental and control groups. The validation methods included sensitivity analysis, correlation analysis and effectiveness analysis.

The sensitivity analysis demonstrated the D_Div index’s ability to differentiate interdisciplinary breakthrough papers from various categories of papers. This index not only retains the strengths of the existing index in identifying breakthrough innovation but also captures interdisciplinary characteristics. The correlation analysis revealed a significant correlation (correlation coefficient = 0.555) between the interdisciplinary attributes of scientific research and the occurrence of breakthrough innovation. The effectiveness analysis showed that the D_Div index reached the highest prediction accuracy of 0.8. Furthermore, the D_Div index outperforms the traditional DI index in terms of accuracy when it comes to identifying interdisciplinary breakthrough innovation.

This study proposed a practical and effective index that combines interdisciplinary and disruptive dimensions for detecting interdisciplinary breakthrough innovation. The identification and measurement of interdisciplinary breakthrough innovation play a crucial role in facilitating the integration of multidisciplinary knowledge, thereby accelerating the scientific breakthrough process.